{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d573c906",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute this cell to install dependencies\n",
    "%pip install sf-hamilton[visualization]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a492fd8c",
   "metadata": {},
   "source": [
    "# Running the DAG in your notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/hamilton_ui/notebook.ipynb) [![GitHub badge](https://img.shields.io/badge/github-view_source-2b3137?logo=github)](https://github.com/apache/hamilton/blob/main/examples/hamilton_ui/notebook.ipynb)\n",
    "\n",
    "\n",
    "This notebook uses the existing module definitions to build the DAG and run it in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6545e23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T18:02:20.935173Z",
     "start_time": "2024-05-27T18:02:12.447366Z"
    }
   },
   "outputs": [],
   "source": [
    "from components import feature_transforms, iris_loader, models\n",
    "from hamilton_sdk import adapters\n",
    "\n",
    "from hamilton import driver as h_driver\n",
    "from hamilton.io.materialization import to\n",
    "from hamilton.lifecycle import PrintLnHook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "488727fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T18:04:30.021612Z",
     "start_time": "2024-05-27T18:04:29.739958Z"
    }
   },
   "outputs": [
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       "</svg>\n"
      ],
      "text/plain": [
       "<hamilton.driver.Driver at 0x151f658b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up config and inputs\n",
    "config = {\"case\": \"parquet\"}\n",
    "dag_name = \"machine_learning_dag\"\n",
    "email = \"elijah@dagworks.io\"  # your email\n",
    "project_id = 38 # your project id\n",
    "\n",
    "# create tracker object\n",
    "tracker = adapters.HamiltonTracker(\n",
    "    username=email,\n",
    "    project_id=project_id,\n",
    "    dag_name=dag_name,\n",
    "    tags={\n",
    "        \"template\": \"machine_learning\",\n",
    "        \"loading_data_from\": \"parquet\",\n",
    "        \"TODO\": \"add_more_tags_to_find_your_run_later\",\n",
    "    },\n",
    ")\n",
    "# create the driver object\n",
    "dr = (\n",
    "    h_driver.Builder()\n",
    "    .with_config(config)  # this shapes the DAG\n",
    "    .with_modules(iris_loader, feature_transforms, models)\n",
    "    .with_adapters(tracker, PrintLnHook(verbosity=1))\n",
    "    .build()\n",
    ")\n",
    "dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec464dc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T18:05:42.223326Z",
     "start_time": "2024-05-27T18:05:42.218761Z"
    }
   },
   "outputs": [],
   "source": [
    "# set up what inputs and outputs we want\n",
    "inputs = {}\n",
    "outputs_to_materialize = [\n",
    "    to.parquet(\n",
    "            id=\"data_set_v1_saver\",\n",
    "            path=\"data_set_v1.parquet\",\n",
    "            dependencies=[\"data_set_v1\"],\n",
    "        ),\n",
    "    to.pickle(\n",
    "        id=\"svm_model_saver\",\n",
    "        path=\"svm_model.pkl\",\n",
    "        dependencies=[\"svm_model\"],\n",
    "    ),\n",
    "    to.pickle(\n",
    "        id=\"lr_model_saver\",\n",
    "        path=\"lr_model.pkl\",\n",
    "        dependencies=[\"lr_model\"],\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2120dd32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T18:05:43.496155Z",
     "start_time": "2024-05-27T18:05:42.642157Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Capturing execution run. Results can be found at http://localhost:8242/dashboard/project/38/runs/85\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing node: svm_model.prefit_clf.\n",
      "Finished debugging node: svm_model.prefit_clf in 398μs. Status: Success.\n",
      "Executing node: iris_df.load_data.iris_data_raw.\n",
      "Finished debugging node: iris_df.load_data.iris_data_raw in 5.8ms. Status: Success.\n",
      "Executing node: iris_df.select_data.iris_data_raw.\n",
      "Finished debugging node: iris_df.select_data.iris_data_raw in 63.4ms. Status: Success.\n",
      "Executing node: iris_df.\n",
      "Finished debugging node: iris_df in 71ms. Status: Success.\n",
      "Executing node: sepal_length_cm.\n",
      "Finished debugging node: sepal_length_cm in 16.5ms. Status: Success.\n",
      "Executing node: sepal_length_cm_mean.\n",
      "Finished debugging node: sepal_length_cm_mean in 253μs. Status: Success.\n",
      "Executing node: sepal_length_cm_std.\n",
      "Finished debugging node: sepal_length_cm_std in 258μs. Status: Success.\n",
      "Executing node: sepal_length_cm_normalized.\n",
      "Finished debugging node: sepal_length_cm_normalized in 17.7ms. Status: Success.\n",
      "Executing node: sepal_width_cm.\n",
      "Finished debugging node: sepal_width_cm in 18.2ms. Status: Success.\n",
      "Executing node: sepal_width_cm_mean.\n",
      "Finished debugging node: sepal_width_cm_mean in 209μs. Status: Success.\n",
      "Executing node: sepal_width_cm_std.\n",
      "Finished debugging node: sepal_width_cm_std in 258μs. Status: Success.\n",
      "Executing node: sepal_width_cm_normalized.\n",
      "Finished debugging node: sepal_width_cm_normalized in 20.5ms. Status: Success.\n",
      "Executing node: petal_length_cm.\n",
      "Finished debugging node: petal_length_cm in 18.9ms. Status: Success.\n",
      "Executing node: petal_length_cm_mean.\n",
      "Finished debugging node: petal_length_cm_mean in 271μs. Status: Success.\n",
      "Executing node: petal_length_cm_std.\n",
      "Finished debugging node: petal_length_cm_std in 244μs. Status: Success.\n",
      "Executing node: petal_length_cm_normalized.\n",
      "Finished debugging node: petal_length_cm_normalized in 10.3ms. Status: Success.\n",
      "Executing node: petal_width_cm.\n",
      "Finished debugging node: petal_width_cm in 17.5ms. Status: Success.\n",
      "Executing node: petal_width_cm_mean.\n",
      "Finished debugging node: petal_width_cm_mean in 193μs. Status: Success.\n",
      "Executing node: petal_width_cm_std.\n",
      "Finished debugging node: petal_width_cm_std in 232μs. Status: Success.\n",
      "Executing node: petal_width_cm_normalized.\n",
      "Finished debugging node: petal_width_cm_normalized in 15.9ms. Status: Success.\n",
      "Executing node: target_class.\n",
      "Finished debugging node: target_class in 9.69ms. Status: Success.\n",
      "Executing node: data_set_v1.\n",
      "Finished debugging node: data_set_v1 in 58.9ms. Status: Success.\n",
      "Executing node: svm_model.data_set.\n",
      "Finished debugging node: svm_model.data_set in 29.7ms. Status: Success.\n",
      "Executing node: svm_model.test_size_fraction.\n",
      "Finished debugging node: svm_model.test_size_fraction in 71μs. Status: Success.\n",
      "Executing node: svm_model.shuffle_train_test_split.\n",
      "Finished debugging node: svm_model.shuffle_train_test_split in 56μs. Status: Success.\n",
      "Executing node: svm_model.train_test_split_func.\n",
      "Finished debugging node: svm_model.train_test_split_func in 67ms. Status: Success.\n",
      "Executing node: svm_model.X_train.\n",
      "Finished debugging node: svm_model.X_train in 27.9ms. Status: Success.\n",
      "Executing node: svm_model.y_train.\n",
      "Finished debugging node: svm_model.y_train in 1.74ms. Status: Success.\n",
      "Executing node: svm_model.fit_clf.\n",
      "Finished debugging node: svm_model.fit_clf in 3.1ms. Status: Success.\n",
      "Executing node: svm_model.training_accuracy.\n",
      "Finished debugging node: svm_model.training_accuracy in 3.3ms. Status: Success.\n",
      "Executing node: svm_model.X_test.\n",
      "Finished debugging node: svm_model.X_test in 26.1ms. Status: Success.\n",
      "Executing node: svm_model.y_test.\n",
      "Finished debugging node: svm_model.y_test in 1.52ms. Status: Success.\n",
      "Executing node: svm_model.testing_accuracy.\n",
      "Finished debugging node: svm_model.testing_accuracy in 2.05ms. Status: Success.\n",
      "Executing node: svm_model.\n",
      "Finished debugging node: svm_model in 437μs. Status: Success.\n",
      "Executing node: lr_model.penalty.\n",
      "Finished debugging node: lr_model.penalty in 64.1μs. Status: Success.\n",
      "Executing node: lr_model.prefit_clf.\n",
      "Finished debugging node: lr_model.prefit_clf in 148μs. Status: Success.\n",
      "Executing node: lr_model.data_set.\n",
      "Finished debugging node: lr_model.data_set in 29.7ms. Status: Success.\n",
      "Executing node: lr_model.test_size_fraction.\n",
      "Finished debugging node: lr_model.test_size_fraction in 63.2μs. Status: Success.\n",
      "Executing node: lr_model.shuffle_train_test_split.\n",
      "Finished debugging node: lr_model.shuffle_train_test_split in 52μs. Status: Success.\n",
      "Executing node: lr_model.train_test_split_func.\n",
      "Finished debugging node: lr_model.train_test_split_func in 54.7ms. Status: Success.\n",
      "Executing node: lr_model.X_train.\n",
      "Finished debugging node: lr_model.X_train in 25.2ms. Status: Success.\n",
      "Executing node: lr_model.y_train.\n",
      "Finished debugging node: lr_model.y_train in 1.5ms. Status: Success.\n",
      "Executing node: lr_model.fit_clf.\n",
      "Finished debugging node: lr_model.fit_clf in 6.59ms. Status: Success.\n",
      "Executing node: lr_model.training_accuracy.\n",
      "Finished debugging node: lr_model.training_accuracy in 1.75ms. Status: Success.\n",
      "Executing node: lr_model.X_test.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Captured execution run. Results can be found at http://localhost:8242/dashboard/project/38/runs/85\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished debugging node: lr_model.X_test in 25.7ms. Status: Success.\n",
      "Executing node: lr_model.y_test.\n",
      "Finished debugging node: lr_model.y_test in 1.61ms. Status: Success.\n",
      "Executing node: lr_model.testing_accuracy.\n",
      "Finished debugging node: lr_model.testing_accuracy in 1.64ms. Status: Success.\n",
      "Executing node: lr_model.\n",
      "Finished debugging node: lr_model in 340μs. Status: Success.\n",
      "Executing node: best_model.\n",
      "Finished debugging node: best_model in 290μs. Status: Success.\n",
      "Executing node: data_set_v1_saver.\n",
      "Finished debugging node: data_set_v1_saver in 5.22ms. Status: Success.\n",
      "Executing node: svm_model_saver.\n",
      "Finished debugging node: svm_model_saver in 589μs. Status: Success.\n",
      "Executing node: lr_model_saver.\n",
      "Finished debugging node: lr_model_saver in 387μs. Status: Success.\n"
     ]
    }
   ],
   "source": [
    "# execute the DAG and materialize a few things from it\n",
    "metadata, result = dr.materialize(\n",
    "    # This approach helps centralize & standardize how objects are read/written and also how metadata\n",
    "    # about them is captured. This is useful for tracking lineage and provenance.\n",
    "    *outputs_to_materialize,\n",
    "    additional_vars=[\"best_model\"],\n",
    "    inputs=inputs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bb3cdd1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T18:05:45.811527Z",
     "start_time": "2024-05-27T18:05:45.803062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_set_v1_saver': {'file_metadata': {'size': 6386,\n",
       "   'path': 'data_set_v1.parquet',\n",
       "   'last_modified': 1716833143.4420505,\n",
       "   'timestamp': 1716858343.44226,\n",
       "   'scheme': '',\n",
       "   'notes': ''},\n",
       "  'dataframe_metadata': {'rows': 150,\n",
       "   'columns': 5,\n",
       "   'column_names': ['sepal_length_cm_normalized',\n",
       "    'sepal_width_cm_normalized',\n",
       "    'petal_length_cm_normalized',\n",
       "    'petal_width_cm_normalized',\n",
       "    'target_class'],\n",
       "   'datatypes': ['float64', 'float64', 'float64', 'float64', 'object']}},\n",
       " 'svm_model_saver': {'file_metadata': {'size': 7697,\n",
       "   'path': 'svm_model.pkl',\n",
       "   'last_modified': 1716833143.4430761,\n",
       "   'timestamp': 1716858343.443196,\n",
       "   'scheme': '',\n",
       "   'notes': ''}},\n",
       " 'lr_model_saver': {'file_metadata': {'size': 1109,\n",
       "   'path': 'lr_model.pkl',\n",
       "   'last_modified': 1716833143.4436038,\n",
       "   'timestamp': 1716858343.443672,\n",
       "   'scheme': '',\n",
       "   'notes': ''}}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07513852",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T18:05:48.332870Z",
     "start_time": "2024-05-27T18:05:48.327916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'best_model': {'logistic': LogisticRegression(),\n",
       "  'training_accuracy': 0.95,\n",
       "  'testing_accuracy': 0.9666666666666667}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a26a515",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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